{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad8b0ed1",
   "metadata": {},
   "source": [
    "# python基础入门11_  MatplotLib数据分析\n",
    "\n",
    "Matplotlib 是一个用于创建图表的库，它提供了多种图表类型和可视化选项。以下是一些常见的 Matplotlib 功能：\n",
    "\n",
    "线图(Line graph):通过输入一维数据(如一组变量的数值),Matplotlib 可以生成线图，以显示数据随时间或其他连续变量的变化趋势。\n",
    "\n",
    "散点图(Scatter plot):通过输入二维数据(如两组变量的观测值),Matplotlib 可以生成散点图，展示两个或更多组之间的关系。\n",
    "\n",
    "柱状图(Bar chart):通过输入一维数据(如一组变量的数值),Matplotlib 可以生成柱状图，以直观地比较不同类别的数据。\n",
    "\n",
    "直方图(Histogram):通过输入二维数据(如两组变量的观测值),Matplotlib 可以生成直方图，展示数据的分布情况。\n",
    "\n",
    "箱线图(Box Plot):通过输入一维数据(如一组变量的数值),Matplotlib 可以生成箱线图，以直观地比较不同类别的数据。\n",
    "\n",
    "热力图(Heat map):通过输入一维数据(如一组变量的数值),Matplotlib 可以生成热力图，显示数据之间的相关性。\n",
    "\n",
    "小提琴图(Gir figure):一种用于表示数据的图形，通常用于表示分类数据。\n",
    "\n",
    "饼图（pie chart）:一种用于表示数据的图形，通常用于表示数据所占比例。\n",
    "\n",
    "这些只是 Matplotlib 提供的部分图表类型。要查看所有可能的图表类型和选项，请查阅官方文档：https://www.matplotlib.org/\n",
    "\n",
    "## 安装方法：\n",
    " 启动anaconda prompt在窗口中输入指令下载安装matplotlib 库：\n",
    " \n",
    " conda install -c conda-forge matplotlib\n",
    " \n",
    " 或者\n",
    " \n",
    " pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a652703",
   "metadata": {},
   "source": [
    "## 导入函数库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f0b91fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "#在Jupyter中显示图像，但省略plt.show()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72823206",
   "metadata": {},
   "source": [
    "## 线图\n",
    "通过输入一维数据(如一组变量的数值),Matplotlib 可以生成线图，以显示数据随时间或其他连续变量的变化趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6e6d9edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2d Diagram')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot y versus x as lines and/or markers.\n",
    "\n",
    "#生成数据\n",
    "x=np.arange(0,10)\n",
    "\n",
    "y=np.arange(10,20)\n",
    "\n",
    "#绘图\n",
    "plt.plot(x,y,'bo',linestyle='dashed',linewidth=1,markersize=15)\n",
    "##第三个参数表示散点的颜色和形状，rgb是颜色，xo*等是点的形状。linestyle：solid是实线\n",
    "\n",
    "\n",
    "#设置标签与标题\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('2d Diagram')\n",
    "\n",
    "#保存图片\n",
    "#plt.savefig('Test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e347850e",
   "metadata": {},
   "source": [
    "## 分割子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "861f1f14",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28842e5c1f0>]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Creating Subplots\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(x,y,'r--')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(x,y,'g*-')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(x,y,'bo')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(x,y,'go')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4b1869a3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28844e4c070>]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3,3,1)\n",
    "plt.plot(x,y,'r--')\n",
    "plt.subplot(3,3,2)\n",
    "plt.plot(x,y,'g*-')\n",
    "plt.subplot(3,3,3)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(3,3,4)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(3,3,5)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(3,3,6)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(3,3,7)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(3,3,8)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(3,3,9)\n",
    "plt.plot(x,y,'go')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "b270298e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x288473b0190>]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(4,4,1)\n",
    "plt.plot(x,y,'r--')\n",
    "plt.subplot(4,4,2)\n",
    "plt.plot(x,y,'g*-')\n",
    "plt.subplot(4,4,3)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(4,4,4)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,5)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,6)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,7)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,8)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,9)\n",
    "plt.plot(x,y,'go')\n",
    "plt.subplot(4,4,10)\n",
    "plt.plot(x,y,'r--')\n",
    "plt.subplot(4,4,11)\n",
    "plt.plot(x,y,'g*-')\n",
    "plt.subplot(4,4,12)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(4,4,13)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(4,4,14)\n",
    "plt.plot(x,y,'g*-')\n",
    "plt.subplot(4,4,15)\n",
    "plt.plot(x,y,'bo')\n",
    "plt.subplot(4,4,16)\n",
    "plt.plot(x,y,'bo')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122c2c81",
   "metadata": {},
   "source": [
    "## 绘制函数图像\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d7f99ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2d Diagram')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot y versus x as lines and/or markers.\n",
    "\n",
    "#生成数据\n",
    "x=np.arange(-10,10)\n",
    "#设置函数\n",
    "y=x**3\n",
    "\n",
    "#绘图\n",
    "plt.plot(x,y,'bo',linestyle='dashed',linewidth=1,markersize=5)\n",
    "##第三个参数表示散点的颜色和形状，rgb是颜色，xo*等是点的形状。linestyle：solid是实线\n",
    "\n",
    "\n",
    "#设置标签与标题\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y axis')\n",
    "plt.title('2d Diagram')\n",
    "\n",
    "#保存图片\n",
    "#plt.savefig('Test.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "353aae78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 3 * x + 5 #y=ax+b\n",
    "plt.title(\"Matplotlib demo\") \n",
    "plt.xlabel(\"x axis caption\") \n",
    "plt.ylabel(\"y axis caption\") \n",
    "plt.plot(x,y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "1f878b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b181fd39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4R2PMJQY5azfGfHXA90fwjOAJGb89dJGsyXEsz5lidSjvE+V0cP+Safxi7znau/sCuj6vCq7fHLzAtGR75ti9i6fxQkUNV3r6SQzg+rzKOn5/ljTGtBhjNnkLflhq7+7jneMN3Lt4Gg7HxE+hPFYfXDqd3n43vz8Stv8Lwl5bZx/bTzZw/xJ75tgHlk6nu8/NFp36I2zYp4FoQ1uO1tHrcnPfkmlWhzKopdkpZKfG89tDF0feWNnS79+7RJ/L2K6147MiZwrTkuP4jbYRw4YW/WG8eugiM1LiWZ6TYnUogxIR7l6Yxe6qRtq7+6wOR/nhN4cukJOawGKLpl0YicMh3L9kGu+caKCtS3MsHGjRH0Jbl+dj972Ls4KyOpa/7l6URZ/L6CieENTW2cfuqibuWzItJHJs23HNsXCgRX8IWyvr6HMZ7l1sz9aOz7LsKWRMiuX372lfP9RsPVaHy224a2GW1aEMS3MsvGjRH8KbR+uYOjmWQotm1Bwth0O4a+FU3j7WoDfRhJg33vPk2BKbtnZ8HA7hjgVT2XZccywcaNEfRHefi3dONLBx/lRbjqgY6O6F0+jqc7H9hN5EEyp8OXbngqyQyLG7FmbR2evS+Z7CgBb9QeyuaqSz18WdNv/Y7VOcl0pyfDSv68fvkLHzZCNdfS7uXBichc/HqyQvjUmxUdriCQNa9Afx5tE6kmKjWJWXanUooxLtdLBx/lS2HK2jz6Xr54aCN45eYlJcFMW5aVaHMioxUQ423JTJlsp6+jXHQpoW/QHcbsObR+u5dV4GsVFOq8MZtTsWTKW9u5/yMy1Wh6JG0O9ys6WynttuyrTVfE4juWthFs1Xeqk4qzkWykIn44Jkf00rjR093LkgND52+6wrSCfG6eCtY3rnpN1VnG2h+Uqv7UftDLR+XgYxTgdvHtUcC2Va9Ad482gdUQ5hvU2mUR6txNgoVuWnsVXH69veW8fqiXYKt8y1x3oRo5UYG0VxXipv6Xj9kKZFf4CtlXVXL4yGmtvmZVDdcIXTjVesDkUN461j9azMTSUpBCcwu+2mTKobrnC2SXMsVGnRv05Ncycn6ztss1jKWN12k6cl9Zae7dtW6OeYJ27NsdClRf8627zj3DfcFJpvyJy0BAoyk7Svb2O+qQxuC9Ecm5WWSF5Gohb9EKZF/zrbjtWTk5pAXnqi1aH47bb5meypbuayTsBmS28dq2dWWgK5oZxj8zw5dqWn3+pQlB+06Ht197nYVdXIhnkZtp78aiS33zSVfrdh+wm9c9Juunpd7K5qYsO8zJDOsdtuyqTX5Wan3p0bkvwu+iLyrIiUisiTQ/w8SkTOicg279fi0exnlT2nm+nuc7M+RD92+yzPSWFyXBTvnNCP33ZTWt1IT787ZFs7PkWzPRehdWbX0ORX0ReRhwCnMaYEyBORgkE2WwI8b4xZ7/06PMr9LLHteD2xUQ5K8kLjDsmhRDkdrCvI4J0TDVxbj17ZwVvH6omPdlIcInd6DyUmysG6gnTePl6vORaC/D3TXw9s8j5+A1g7yDargPtFZK/37D5qNPuJyBMiUi4i5Q0NwZtAbNvxBkry04iLDp27cIdyy9x06tp7OFHXYXUoyssYw7bjDayZkxZSd3oP5da5GZpjIcrfop8I1HofNwOD3b76LrDRGLMSiAbuHc1+xphnjDFFxpiijIzg3LxyptEztn19iN0sMxTfTT/a4rGPM02dnG/p4tYwyzGd2TX0+Fv0O4B47+OkIV7nkDHGt3hrOVAwyv2CbsdJT+KG2l24Q5mWHM+8qZP0Yq6N+IrjuoLwKPrTU+KZk5nE9pNa9EONv0W3gmutmULgzCDbPCcihSLiBB4EDo5yv6DbfrKR7NR4ZqUlWB1KwNwyN529p5vp7NVhdXaw/UQDOakJzA7hoZoD3VKQwZ7TzXT16sIqocTfor8ZeFxEngYeBt4TkacGbPMN4DngAFBqjNkyyH6v+nn8gOlzuSmtamJdQWgP1Rzo1rmeYXVl1U1WhxLxevvdlFY3ccvcdKtDCahb5qbT2+9mz2nNsVDiV9E3xrTjuShbBmwwxhw0xjw5YJsjxpglxpjFxpivDbFf23iCD4T951rp6OnnloLwekMWzZ5CXLRDWzw2UHG2hc5eV9i0dnyKc9OIidIcCzV+z/hkjGnh2kicCd9vouw42YBDoCQ/vIp+XLSTkrw03tELbZbbfrKBKIewOj+0hwMPFB/jpDg3Vfv6IcYWF1KttP1kI0uzU0JyVs2RrCvI4HTjFc63dFodSkTbcbKB5TlTmBQXfjl2S0EGp+o7uNDaZXUoapQiuui3dvZy6Hxr2H3s9lnrbVntPqU9V6s0dfRwpLY97Pr5Pr6hmzv0bD9kRHTR33WqCWMI2zdkQWYSmZNi2aFzpFhmV5XnH9y1YXpiMXdqEulJsezSE4uQEdFFf8fJBibFRlE4M8XqUCaEiLB2Tjq7TzXiduvt8lbYdbKRyXFRLJ6RbHUoE8KTY2ns0hwLGRFd9HdVNbIqP40oZ/j+GdbMSafpSi/HLl22OpSIY4xh56lGSvLTcDrCZzjwQL4cO16nORYKwrfajaCmuZOa5i7WhNmIioHWzPG0rnae0p5rsJ1r7qS2tevq/4Nw5fv9dmkbMSREbNH3JWi4vyGzkuMoyExip/Zcg25nhOTY9JR48tITteiHiIgt+jtPNZI5KZY5mUlWhzLh1sxJZ+/pJnr69Xb5YNp9qomsyXEhvRLbaK2Zk86e08309rutDkWNICKLvtttKK1qYs2c9LCaemEoa+ek093npuJsi9WhRAy327C7qjFicmzNnHQ6e10cqGm1OhQ1gogs+sfrLtN0pTfs7pAcSnFeKk6HUFqlLZ5gOXqxnZbOPtYWREaOleSl4RB0CcUQEJFFP1L6+T6T4qJZPCOZ3Vr0g8aXY6vDbHqPoSQneHJM+/r2F5FFf3dVE7npiUxPiR954zCxOj+NgzWtXOnRqZaDYVdVE3Myk5g6Oc7qUIKmJD9dcywERFzR73O52VPdFDGtHZ/V+en0uw3vnmm2OpSw1+dyU36mOQJzLI1+t6Fcrx3ZWsQV/UPn27jS64qYj90+K2ZNIcbp0L5+EBw630pnr4uSvMgq+kWzpxDtFHZXaYvHziKu6PsWFSmJsLOw+Bgny3JStK8fBL4J7lZFWNFPiIliaXYKZZpjtuZ30ReRZ0WkVESeHOLnySLymoi8ISKviEiMiESJyDkR2eb9Wux/6P4prWripqxJpCbGBPvQlludn86RC220dfZZHUpYK61uYv60yUyJwBwryU/ncG0b7d2aY3blV9EXkYcApzGmBMgTkYJBNvsY8LQx5k7gEnA3sAR43hiz3vt12N/A/dHT76L8bHPEnYH5rJ6ThjFQpsvbTZjuPhcVZ1sirrXjU5KXhtvA3mq9dmRX/p7pr+fa6ldvcG2x86uMMd83xrzp/TYDqAdWAfeLyF7vJ4UbVu4SkSdEpFxEyhsaAjtfzMGaNrr73BHX2vEpnJlCfLRT+/oTaP+5Vnr63RF3EddnWU4KsVEObSPamL9FPxGo9T5uBqYOtaGIlABTjDFlwLvARmPMSiAauHfg9saYZ4wxRcaYooyMwM5BXlrVhAisyo3MN2RMlIOi2VP0QtsEKq1uwiGwMi/V6lAsERftZMWsKZRWa9G3K3+LfgfgG+SeNNTriEgq8G/Ap7xPHTLGXPQ+LgcGawtNmNLqRhZMm0xyQvgtWzdaJflpnKjroKmjx+pQwlJZVROLZyQzOQyXRhyt1flpVF5sp/lKr9WhqEH4W/QruNbSKQTODNxARGKAF4CvGGPOep9+TkQKRcQJPAgc9PP4Y9bd52LfudaI7bX6+K5n7DmtPddA6+p1sb+mhVUR2trx8bVP9+q1I1vyt+hvBh4XkaeBh4H3ROSpAdv8MbAc+Jp3pM4jwDeA54ADQKkxZoufxx+zfeda6O2P3H6+z+IZySTEOK8OXVWBU3G2hT6XifgTi8UzPNeOyvRiri3dcCF1NIwx7SKyHrgD+EdjzCUGnLUbY34A/GCQ3Zf4c8zxKqtuxiFwc25k9lp9op0OimanatGfAGXVTTgdQtHsyM4x37UjzTF78nucvjGmxRizyVvwba+sWnutPiV5nr5+o/b1A8qXY0mxfp1LhZVVeWkcu3RZ+/o2FBF35Hb3uThwrpXiCP/Y7bPKO7Jkj378DpjO3n4Onm+N2HtABvLlmPb17Sciiv6+cy30utxXEzHSLZqRTKL29QNq39lW+lxGc8xL+/r2FRFF39fPj/Req4/29QNP+/nvp319+4qQot/EIu3nv09Jfhon6ztouKx9/UDQfv6NfH19vSfEXsK+6Pv6+dprfT/f32OvjtcfN+3nD+5aX19zzE7CvujvP9dKr8tNcYQP1Rxo0fTJ2tcPEO3nD+5aX19zzE7CvuiXeedC0V7r+0U5HayYncoeHV0xbtrPH5yvr693f9tLRBT9hdOTSY7Xfv5AxbmpOg9PAOw53cSi6ZO1nz+I4txUHa9vM2Fd9Lv7XOyvadXWzhC0rz9+Xb0uDta0aT9/CMWaY7YT1kX/QE0rvf1uvSlrCEtmJhMf7dSP3+Ow33sPSLH28we1ZGYysVEObSPaSFgX/T3VzYjASu21Dira6WDFLB1LPR5lp/UekOHERnnm19e7v+0jvIv+6SbmZ0X2/PkjWZXn6bm2aM/VL3uqm1gwfbLeAzKM4tw0Ki+169rMNhG2Rb+n38W+cy36sXsEV3uuZ/RMbKyuXTPS9uFwivNSMUZzzC7CtugfOu9ZD1ffkMPz9Vy1xTN2B73XjPQi7vCWZqcQE+Vgj+aYLYRt0fclmI7cGV5slJPlOdpz9cee03rNaDTiop0sy07RAQM2Eb5F/3QzN2VNYkpijNWh2F5xXqr2XP1QVt3ETXrNaFSK89J470Ib7d2aY1bzu+iLyLMiUioiT45lm9HsN159LjcVZ1v0LH+UinPTMAbe1Z7rqPX2uz3XjDTHRmVVbipuAxVnWqwOJeL5VfRF5CHAaYwpAfJEpGA024xmv0A4XNtGZ69Lx+eP0rIcb89Vx1KP2qHzrXT36RoNo7UsZwrRTtFrRzbg75n+emCT9/EbwNpRbjPifiLyhIiUi0h5Q0ODX8G1d/WRm57ISj0LG5W4aCdLtec6Jr6/1UodKDAq8TFOCmemUKY5Zjl/i34iUOt93AxMHeU2I+5njHnGGFNkjCnKyMjwK7j18zJ5+6/Wk54U69f+kWhVbipHatu4rD3XUSmrbmLe1Emk6jWjUSvO8+RYR0+/1aFENH+LfgcQ732cNMTrDLbNaPZTFijOS8NtoPys9lxHcvWakbZ2xmRVXhout6FCc8xS/hbdCq61ZgqBM6PcZjT7KQss9/ZcdejmyI74rhlpa2dMVsyaQpRDdLy+xfydC3YzsENEpgP3AI+KyFPGmCeH2WYVYAZ5TtlAfIyTJTNT9ELbKFzr5+uZ/lgkxESxeGayXjuymF9n+saYdjwXZcuADcaYgwMK/mDbtA32nP+hq0Arzk3lcG0bV7TnOqw91U3kZySSMUmvGY1VcW4ah8630tXrsjoUWztZd5nefveEvLbfPXVjTIsxZpMx5tJYthnNfsoaxdpzHZHLbSg/06JTL/ipOC+VPpdh3znNsaH0udw8+L1d/P2rRyfk9fVCqrqqaNYUnA7R8frDOHqhncs9/XoPiJ98OaZtxKEdqW3jSq+LmyeofahFX12VGBvF4hnJlOnF3CGV6ZxO4zIpLppF0yfrgIFh+K55TNRAAS366n1W5WnPdTh7TjeRl57I1MlxVocSslblpXGgppXuPs2xwUz0NSMt+up9tOc6NJfbsOd0s47PH6fivFR6XW7NsUH0u9y8O8HXjLToq/fRnuvQKi+2c7m7Xy/ijlPR7FQcgrYRB3H0YjsdE3zNSIu+eh/tuQ7tWj9fi/54TI6LZuH0ZL1JaxC+HFs1gdeMtOirG2jPdXBl1c3MTksgK1n7+eO1Ki+V/ZpjN9hT3UxeeiKZE3jNSIu+uoH2XG/kchv2nm7S1k6AFOem0dvvZv+5VqtDsQ1PjjVP+HBgLfrqBtpzvVHlxXbau/v1Im6A3Jybigh6T8h1Ki967gGZ6DUatOirG0yOi2bRjGS9mHudiR47HWmS46NZMG2y5th1gnXNSIu+GtSqvDQOnNOeq09ZdROz0hKYnhI/8sZqVFblpbFPc+yq0qomctMTJ/yakRZ9NaiSvDRPX1/n4fGMz69u0rtwA2xVnvb1fXz9/GBcM9KirwZVNNszXr9UP35f7eeX5GtrJ5BW5nquHWmOwXsX2rjcE5wc06KvBjVJ+/pX+f4GJXnpFkcSXpLjPeP1Ncc8rR2Y2PH5Plr01ZBW5aVyoEbn4QlWrzUSleTrtSPwfNrJz5jY8fk+WvTVkEry0uhzGcrPRu7QzX6XO2i91ki0yntPSCSv4dDvcvPu6eagtQ/HXPRF5FkRKRWRJ4fZJllEXhORN0TkFRGJEZEoETknItu8X4vHF7qaaEWzUyN+Hp73vPPnaz9/YtysOeZZra7XFbQTizEVfRF5CHAaY0qAPBEpGGLTjwFPG2PuBC4BdwNLgOeNMeu9X4fHE7iaeEne+fV9/cZI5LvIONE3zEQq37UjzTHsWfTxrG+7yfv4DWDtYBsZY75vjHnT+20GUI9nEfT7RWSv99PCoIuyi8gTIlIuIuUNDQ1jDE8FWkl+GofOt9ERoevmllZ5e62TtJ8/UUry0jh4vpXO3sjMsbLqZgoyk0hPCs6ay8MWfRH54XXtmG3AF4Fa74+bgakj7F8CTDHGlAHvAhuNMSuBaODewfYxxjxjjCkyxhRlZGSM7bdRAbc6P41+t+HdM5HX1+9zuSk/E7xea6Ra5V3DofxM5PX1e/uD28+HEYq+MeYz17Vj1gPfBXy3JCYNt7+IpAL/BnzK+9QhY8xF7+NyYKjWkLKRolmpxDgdEfnx29dr1aGaE+vm2alEReg9IQfPt9LV52J1fvBybKztnQqutXQKgTODbSQiMcALwFeMMWe9Tz8nIoUi4gQeBA6OOVoVdPExTpblpLC7qtHqUIJu9ynP76z9/ImVGBvF0uyUq3/vSLL7VBMiwc2xsRb9zcDjIvI08DDwqogsEJGnBmz3x8By4Gve1tAjwDeA54ADQKkxZsu4IldBszo/nfcutNPa2Wt1KEG161QT86dNJi1IvdZItnpOOodr22jr6rM6lKDaVdXIounJpCTEBO2YYyr6xph2PBdzy4ANxpg2Y8xRY8yTA7b7gTFmynWtof8xxhwxxiwxxiw2xnwtcL+Cmmir56RhDBE1rK67z0XFuRbWaD8/KFbnp+E2RNRqWl29Lvafa2F1kHNszOP0jTEtxphNxphLExGQsp/CmSnERzvZHUF9/YqzLfT2u1kzR/v5wbAsJ4W4aEdE5Vj52Wb6XCboAwX0jlw1opgoBzfnpkbUG3LXqUaiHMLNOrNmUMRGObl5dmpEXTvaXdVElENYGeQc06KvRmVNfhqn6juob++2OpSg2FXVRGF2Ckmxg95OoibA6vx0TtR1UH85MnJs96lGluWkkBAT3BzToq9GxTekLBLO9tu7+zh8vlX7+UG2Zo7n7x0Jw4Pbuvo4XNtGSRCHavpo0VejsmD6ZJLjo9kZAcPq9lQ34zaeESUqeBZOT2ZyXBS7T4V/0d9T3eTJMQtOLLToq1FxOoQ1c9LYebIRY4zV4UyoXacaiYt2sCwnxepQIorTIazKS2PnqfDPsZ2nGkmIcbI8Z0rQj61FX43a2jkZXGrvpqqhw+pQJtSuU43cPDuV2Cin1aFEnHUF6dS2dnGmqdPqUCbUzpONFOemEhMV/BKsRV+N2roCT7tj58nwbfFcbOviZH3H1d9VBdfaAs98WztPhu9ki7WtXVQ3Xrn6uwabFn01atmpCeSkJoR1X9/3D9o6i96QkW52WgIzUuLZEcYnFr5/0Kw6sdCir8ZkbUE6ZdXN9LncVocyIXacbCQ9KZabsiZZHUpEEhHWFaRTWtVEfxjn2NTJsRRkJllyfC36akzWzUmno6efgzWtVocScG63YdepRtYVpCMiVocTsdYVZHC5p5+D51utDiXg3G7D7qom1syxLse06KsxWZ2fjghh+fG78lI7TVd6WatDNS21Oj8tbHPs6MV2mq/0WnrNSIu+GpPkhGiWzEgOy76+r8is1Yu4lpqSGMPiGclhOWDAl2NrLLgpy0eLvhqzW+ZmsP9cC22d4TUN7s6TjcybOompk3VpRKutnZPO/ppWLneHWY6damDe1ElkWphjWvTVmN06NwO3IazO9rv7XOw906xn+TaxriADl7f/HS6u9PTz7ukWbp1n7cgwLfpqzJZmpzA5Lop3TtRbHUrAlFY30dvv5pa5OlTTDopmTyEpNoptx8NnvP7uqiZ6XW7WW5xjYy76IvKsiJSKyJPDbBMlIueuW1R98Wj3VfYX5XSwriCDd040hM3t8u8cbyAu2kGxTqVsC9FOB2vmpPHO8frwybET9STEOFkxO/hTL1xvTEVfRB4CnMaYEiBPRIZa3HwJ8Px1K2cdHsO+KgTcOjeDuvYejtddtjqUgHj7eD2r89OJi9apF+xi/bxMLrR1c7I+9Kf9MMaw7XgDq/PTLZ/eY6xn+uuBTd7Hb3BtkfSBVgH3i8he79l91Gj3FZEnRKRcRMobGsLno1248bVBwuHj9+nGK5xt6mS9xb1W9X6+/x/bjod+G7G68QrnW7pskWPDFn0R+eF1LZptwBeBWu+Pm4GpQ+z6LrDRGLMSiAbuBRJHs68x5hljTJExpigjw/o/kBpcVnIcN2VN4p0wKPq+orJ+bqbFkajrTUuOZ97USWFxYuH7HW61wTWjYYu+MeYz17Vo1gPfBeK9P04aZv9DxpiL3sflQAHQMcp9VYi4dV4G5Web6ejptzqUcXn7eAN5GYnkpCVYHYoaYP28DN49E/o5tu14PfkZiWSnWp9jYy28FVxryxQCZ4bY7jkRKRQRJ/AgcHAM+6oQcevcDPpcnqkLQlVXr4uy6iY9y7epW+d5cmx3iOfYntPN3GqTHBvr4oybgR0iMh24B1glIguAx4wx14/I+QbwC0CAXxtjtojI5IH7jjt6ZambZ6cyKS6KrZV13LUwy+pw/FLmHapph16rulHRrFQSY5xsO9HAnSGaY7tONdoqx8Z0pm+MacdzQbYM2GCMaTPGHB1Q8DHGHDHGLDHGLDbGfG2ofQPxCyjrRDsd3Do3g7eONeB2h+awureO1RMf7WSlDtW0pZgoz/DgtypDd+jm1mN1JMVGUZxnjxwbc1/dGNNijNlkjLkUzH2VPd0+P5PGjh4O1Ybev+HGGLZW1rGuQIdq2tnGBVO51N7NexfarQ5lzNxuw9bKem6Za/1QTR+9mKrGZf3cTBwCWyvrrA5lzN670M6Ftm42LhhqEJqygw3zMnAIvHk09HLscG0b9Zd72DjfPjmmRV+Ny5TEGIpmpbKlMvTGUm+prEMEbrvJHhfY1ODSkmJZnjOFLSF4YrG1sg6HwIZ59skxLfpq3G6fn0nlxXZqW7usDmVMtlTWsSJnCulJsVaHokawccFUzyezkMuxelbMmsKUxBirQ7lKi74at9u9H13fCqEzsYttXRypbdfWTojwtUe2HgudT5QXWrs4erHdVq0d0KKvAiA/I5Hc9ETeDKEWzxZvf9hub0g1OF+ObQmhvr7vOtftNssxLfpq3ESEOxdOZfepxpBZWOXNynpy0xPJz0i0OhQ1CiLC7TdlUlrVFDJ3575xtM6WOaZFXwXEPYum0e82IXGxrb27j7KqJjbOz9QF0EPIXYuy6HW5eSsEWjwtV3rZXdXEPYuybJdjWvRVQCyZkcy05DheO2L/WzC2HK2j1+XmnsXTrA5FjcGKnClkTorld4cujryxxd48WofLbbjXhjmmRV8FhMMh3LUwi+0nG2z/8ft3hy8yPTmOZdkpVoeixsDhEO5ZlMXbx+u5YvMce+3IRWZOiWfh9MlWh3IDLfoqYO5ZlEVvv5u3bfzxu727j+0nGrl70TTbfexWI7tn8TR6+t22nm65rauPnacauXexPXNMi74KmKLZqaQnxfC6jVs8Wys9rZ37loTm5F2R7mZvjv3usH1bPFsr6+hzGe5ZZM8c06KvAsbpEO5cmMVbx+rp6nVZHc6gXj10iazJcSzLtnadUuUfp7eNaOcce+3IJaYlx1E4M8XqUAalRV8F1H2Lp9HV52LrMfuN4rnc3cf2kw3cszgLh8N+H7vV6Phy7J0T9msjtnf38c6JBu5aaN8c06KvAmpVXhpTJ8eyef8Fq0O5wdbKenr73dxnwxEVavRW5qaSlhjDrw/aL8deP3KJ3n43H1w63epQhqRFXwWU0yF8oHA6247X03Kl1+pw3ufl/bXMSIlneY62dkJZlNPBA4XT2VJZT1uXvW4G3Ly/ltlpCSy18cgwLfoq4D64dAb9bsOrNrrYVt/ezc6TDXxo2QzbfuxWo/fQ8hn09rttdUH3YlsXpdVNPLhshi1H7fiMueiLyLMiUioiTw6zzedEZJv364CI/FBEokTk3HXPLx5f6MquFk6fTEFmEr86UGt1KFf96sAF3AY+tHyG1aGoAFg8I5n8jERe2WefHPv1gQsYAw8utXeOjanoi8hDgNMYUwLkiUjBYNsZY35gjFlvjFkP7AB+BCwBnvc9b4w5PM7YlU2JCA8um8G7Z1qoae60OhwAXtp3nqXZKeRnJFkdigoAEeGh5TPZe6bZNjn2yv5aluWkMDvdXnPtDDTWM/31wCbv4zeAtcNtLCIzgKnGmHI8C6HfLyJ7vZ8WBl2UXUSeEJFyESlvaLDvDRhqeB8o9FzIssPZ/tEL7Ry7dJkP61l+WPFdLN283/ocq7zoybEPLbN/jg1b9L1tGV87ZhvwRcD3F24GRpoz9PPAD7yP3wU2GmNWAtHAvYPtYIx5xhhTZIwpysiwx+rxauyyUxNYlZfK/5TXWL5o+sv7zhPtFO5fYt8RFWrsZk5JoDg3lVf211q+aPqLFeeJckhIjAwbtugbYz5zXTtmPfBdIN7746Th9hcRB7AB2OZ96pAxxnfVpRwYtDWkwsdjxbOoae5i56lGy2Lo7Xez+UAtG+Zl2mr1IhUYHy3KprrxCmXVzZbF0N3n4sWK89y1KIu0EFiFbaztnQqutXQKgTPDbLsO2GOu/RP8nIgUiogTeBA4OMZjqxBz18KpTEmI5vm95yyL4fX3LtHY0ctjxTmWxaAmzv1LppEcH83P9py1LIbfHb5IW1cfHwuRHBtr0d8MPC4iTwMPA6+KyAIReWqQbe8Ctl/3/TeA54ADQKkxZsvYw1WhJDbKyUdWzOTNo3XUX+62JIaflZ5lVloCtxRoqzAcxUV7cuz3Ry5ZlmM/33OOvPRESvLSLDn+WI2p6Btj2vFczC0DNhhj2owxR40xNwzfNMZ81Rjz8nXfHzHGLDHGLDbGfG28gavQ8Acrc+h3G14oPx/0Yx+71M7eM838YfEsHZsfxh4rtjbHKs628Fhxjq3H5l9vzOP0jTEtxphNxhj7TqWobCMvI4lVeak8v/ccriBf0P1Z2Vlioxx8ZMXMoB5XBVd+RhKr89P4xZ7g59gv9pwjJsrBh5eHTo7pHblqwv1RyWzOt3Tx+/eCd57Q0dPPK/tqeaBwul7AjQB/uGoWta1dQV1Ksa2zj5cqznP/4mkhlWNa9NWEu2thFrPTEvjhO1VBG1r3y73nuNLr4vFVs4JyPGWtOxZMZUZKPP8RxBz72Z6zXOl18Sfr8oJyvEDRoq8mnNMhfPqWPA6eb6O0umnCj9fd5+KZ7dWszk+j0MYTX6nAiXY6eOKWPCrOtrD39MQP3+zuc/Hjnae5dW4GC2y4JOJwtOiroPjw8pmkJ8Xww3eqJ/xYL1acp/5yD1/YMGfCj6Xs4+GibNISY/j+tqoJP9YL5TU0Xenlc+vzJ/xYgaZFXwVFXLSTT67J5Z0TDRy90D5hx+lzufnBtiqW5aRQkh8aQ+hUYMTHOPnUWk+OHaltm7Dj9LvcPLOjmmU5KRTnpk7YcSaKFn0VNH9YPItJsVE8/ebxCTvGrw5coLa1iy/eNidkhtCpwHm8xJNj33v71IQd45X9tdQ0d/HZW/NDMse06KugSU6I5nMb8tlSWU9pVeB7+919Lr6z9QQLpk1mw7zMgL++sr/JcdF8cm0urx25RMXZwPf2O3v7+ec3jlOYncId80eaesyetOiroPrUmlymJ8fxzd9VBnwitv/cUU1NcxdP3jc/JM/AVGB85pY8pk6O5Ru/DXyO/Wj7aerae/jb++aH7A1/WvRVUMVFO/nLO+dxuLaN3xwK3BqnF9u6+N7bVdy9MIvVc9ID9roq9CTGRvHXd93EwZpWNgdwau+69m7+450q7l2cRdHs0Ovl+2jRV0H3oWUzWDh9Mv/vtWMBW+P0W68dw2UMX7tvfkBeT4W2Dy2bwZKZyXzr9WNc6ekPyGt+67VjuNyG/333TQF5Pato0VdB53AIf/+hxdRf7uHvfv3euF/v7eP1bD5wgSfW5ZGdmhCACFWocziErz+wgPrLPTz16tFxv95rhy/y8v5aPnNrHrPS7L0y1ki06CtLLM1O4Yu3zeGV/bX8dhxtnktt3fzlpoPclDWJL9ym4/LVNStmpfLZW/N5fm8Nrx7yfwH1i21d/M3Lh1kyM5kv3R76y4Bo0VeW+fyGORTOTOZrrxyhtrVrzPv3u9x86Zf76e5z8e+PLScu2jkBUapQ9uU75rI0O4W/efkQ51vGvpauy234qxcO0tvv5l8fWUq0M/RLZuj/BipkRTsd/MsjS3Ebw+P/uYeGyz2j3tcYw1OvVrL3dDNPPbiIOZm64Lm6UbTTwb/9wTIw8Cc/Laf5Su+o93W7DV99+TC7TjXx9QcWkJcRHjmmRV9ZKi8jif/65M1cbOvm8Wf30No58pvS7TY8ufkI/7X7DJ9ak8tDITStrQq+7NQEvv+HyzndeIXHflRGU8fIJxfGGP72V0f4n/IavnR7AY+uDI1VsUZDi76y3IpZqfzoj4qobrjCQz/YTcXZliG3be3s5S82HeDne87xufX5/O39OlpHjWxdQQY//sTNnGm6wqPPlA07TUNjRw9ffH7/1Rz7i42h38e/nox1GlIRmQq8aIxZN8J2zwILgFeNMU8N9dxwioqKTHl5+ZjiU6Frd1Ujf7XpIBfbu/l4yWweXDaDRdMn43QIde09vHr4It/depL27j7+6s55fF4nVFNjVFrVxBd+sY/mzl4evTmHj6+eRX5GElEO4XyLZz7+p988QWdvP3++cS5/uj40p1oQkQpjTNGgPxtL0ReRKcDzQKYxZvkw2z0EfMAY8wkR+THwD8Digc8ZY04Odzwt+pHncncf33r9GD/fcw5jICk2CmMMV3pdAKwrSOer985n/rTQms5W2UdbVx/f2XKSn5aeweU2xDgdJMVFXe33r5ydyjcfWsSczEkWR+q/QBb9yYAAvzLGrB9mu+8CrxtjficijwLxwLKBzxljfjLIvk8ATwDk5OSsOHvWulXulXUaLvdQVt3E3tPNOB1CXkYiC6cnszwnJSTPvJT91DR3su9cC0cvtNPS2cvimSksz0lhwbTJIZ9jwxX9qBF2/CEw77qn3jLGfGMUf5BEwHf/czOwfIjnbmCMeQZ4Bjxn+iMdSIWnjEmxPFA4nQcKp1sdigpT2akJZKcm8MGlM6wOJaiGLfrGmM/4+bodeM7uAZLwXDAe7DmllFJBNFGFtwJY631cCJwZ4jmllFJBNOyZ/miIyALgMWPMk9c9vRnYISLTgXuAVYAZ5DmllFJB5NeZ/vUXcY0xRwcUfIwx7cB6oAzYYIxpG+w5P2NWSinlp3Gf6Q/FGNMCbBrpOaWUUsGjF1OVUiqCaNFXSqkIokVfKaUiyJjn3gkmEWkA/L0lNx1oDGA4VtDfwR5C/XcI9fhBf4exmmWMyRjsB7Yu+uMhIuVD3YYcKvR3sIdQ/x1CPX7Q3yGQtL2jlFIRRIu+UkpFkHAu+s9YHUAA6O9gD6H+O4R6/KC/Q8CEbU9fKaXUjcL5TF8ppdQAWvSVUiqChGXRF5FnRaRURJ4ceWv7EZFkEXlNRN4QkVdEJMbqmPwhIlNFZL/VcYyHiHxfRB6wOg5/iMgUEfmdiJR7F0QKKd782XHd9yH1vr4+fju9p8Ou6HvX53UaY0qAPBEJxaXsPwY8bYy5E7gE3G1xPP76Z64tnBNyRGQdkGWM+Y3VsfjpceDn3rHhk0TE8jHio+Vdj/uneFbcC7n39cD4sdF7OuyKPp7pm30zeb7BtYVbQoYx5vvGmDe932YA9VbG4w8RuQ24gifBQ46IRAM/As6IyAetjsdPTcAiEUkBsoEaa8MZExfwCNDu/X49ofW+fl/8dnpPh2PRH7gW71QLYxkXESkBphhjyqyOZSy8H13/Fvgbq2MZhz8CjgL/CKwUkS9aHI8/dgKzgC8BlXjeDyHBGNM+YM2NkHpfDxI/YI/3dDgW/bBYi1dEUoF/Az5ldSx++Bvg+8aYVqsDGYdlwDPGmEvAz4ANFsfjj68DnzXGfAM4BnzS4njGI+Tf13Z5T4fcH24UQn4tXu+Z8gvAV4wx/k44Z6WNwOdFZBuwVET+0+J4/HEKyPM+LsL/if+sNAVYLCJOoBjPkqWhKqTf13Z6T4fdzVkiMhnYAWzFuxZvqC3NKCKfA74JHPQ+9QNjzP9YGJLfRGTb9ctrhgoRmQT8GE8bIRr4iDGmdvi97EVEVgI/wdPiKQU+ZIzpsDaqsfHlT6i+r6+L3zbv6bAr+nD1yvkdwHbvx3OlVIjT93VghGXRV0opNbhw7OkrpZQaghZ9pZSKIFr0lVIqgmjRV0qpCKJFXymlIsj/Bx0KOkd/ktSKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the x and y coordinates for points on a sine curve \n",
    "x = np.arange(0, 4 * np.pi, 0.1) \n",
    "y = np.sin(x) \n",
    "plt.title(\"sine wave form\") \n",
    "\n",
    "# Plot the points using matplotlib \n",
    "plt.plot(x, y) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fec97093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Subplot()\n",
    "# Compute the x and y coordinates for points on sine and cosine curves \n",
    "x = np.arange(0, 5 * np.pi, 0.1) \n",
    "y_sin = np.sin(x) \n",
    "y_cos = np.cos(x)  \n",
    "   \n",
    "# Set up a subplot grid that has height 2 and width 1, \n",
    "# and set the first such subplot as active. \n",
    "plt.subplot(2, 2, 1)\n",
    "   \n",
    "# Make the first plot \n",
    "plt.plot(x, y_sin,'r--') \n",
    "plt.title('Sine')  \n",
    "   \n",
    "# Set the second subplot as active, and make the second plot. \n",
    "plt.subplot(2, 2, 2) \n",
    "plt.plot(x, y_cos,'g--') \n",
    "plt.title('Cosine')  \n",
    "   \n",
    "# Show the figure. \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c7fa788",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "435b7960",
   "metadata": {},
   "source": [
    "## 散点图（Scatter plot）\n",
    "\n",
    "通过输入二维数据(如两组变量的观测值),Matplotlib 可以生成散点图，展示两个或更多组之间的关系。\n",
    "\n",
    "散点图是一种在直角坐标系中展示两个变量之间关系的图表，通常用于观察两个变量之间的相关性和离群值。通过散点图，我们可以获取以下三类关键信息：\n",
    "\n",
    "1.判断两个变量之间是否存在数量关联趋势。如果变量之间不存在相互关系，那么在散点图上就会表现为随机分布的离散的点；如果存在某种相关性，那么大部分的数据点就会相对密集并以某种趋势呈现。\n",
    "\n",
    "2.确定这种关联趋势是线性还是非线性的。这有助于我们选择合适的数学模型来描述这两个变量之间的关系。\n",
    "\n",
    "3.观察是否有离群值，并分析这些离群值对建模分析的影响。离群点可能会对统计分析和模型预测产生不良影响，因此需要特别关注。\n",
    "\n",
    "散点图的应用非常广泛，例如在回归分析中，它可以用来表示因变量随自变量而变化的大致趋势，据此可以选择合适的函数对数据点进行拟合。此外，散点图还可以用于评价测序的两个样本或者两组数据之间的差异性和一致性，从图中可以看出基因的差异表达情况及样本相关性关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b29a08ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#生成数据\n",
    "x=np.arange(0,10)\n",
    "y=np.arange(11,21)\n",
    "\n",
    "#绘制图像\n",
    "plt.scatter(x, y ,c=\"g\",marker=\"x\")\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('X轴')\n",
    "plt.ylabel('Y轴')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('散点图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n",
    "\n",
    "#保存图片\n",
    "#plt.savefig('Test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70bafc42",
   "metadata": {},
   "source": [
    "## 柱状图(Bar chart)\n",
    "\n",
    "通过输入一维数据(如一组变量的数值),Matplotlib 可以生成柱状图，以直观地比较不同类别的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "94a685d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据\n",
    "x = ['A', 'B', 'C', 'D', 'E']\n",
    "y = [3, 7, 2, 5, 8]\n",
    "\n",
    "# 创建柱状图\n",
    "plt.bar(x, y)\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('类别')\n",
    "plt.ylabel('数量')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('柱状图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "79508648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Bar plot\n",
    "\n",
    "x = [2,8,10] \n",
    "y = [11,16,9]  \n",
    "\n",
    "x2 = [3,9,11] \n",
    "y2 = [6,15,7] \n",
    "plt.bar(x, y) \n",
    "plt.bar(x2, y2, color = 'g') \n",
    "plt.title('Bar graph') \n",
    "plt.ylabel('Y axis') \n",
    "plt.xlabel('X axis')  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74dfad4d",
   "metadata": {},
   "source": [
    "## 直方图(Histogram)\n",
    "\n",
    "通过输入二维数据(如两组变量的观测值),Matplotlib 可以生成直方图，展示数据的分布情况。\n",
    "\n",
    "\n",
    "直方图和直条图是用于展示数据分布的两种图形方式，但它们在表示数据的方式、数据类型及应用场景等方面存在显著差异。\n",
    "\n",
    "数据表示方式：直方图主要用面积来表示各组频数或频率的多少，其高度与宽度均有意义；而直条图则使用长方形的长度来表示各类别的频数或频率，其宽度（表示类别）则是固定的。\n",
    "\n",
    "数据类型：直条图适用于表示离散型数据，各个类别的频数或频率由独立的垂直条来表示；而直方图则主要用于表示连续型数据或分组数据，将整个数据范围划分为若干连续的区间，每个区间的高度或面积表示该区间的频数或频率。\n",
    "\n",
    "坐标轴：直条图通常有两个坐标轴，横轴用于表示不同的类别或组，纵轴表示频数或频率；而直方图也有两个坐标轴，横轴表示数据的范围或区间，纵轴表示每个区间的频数或频率。\n",
    "\n",
    "特征对比：直方图可直观表示大量数据，分布形状，相邻数据变化，但缺点是原始数据不能显示，频率分布不能估计；直条图能直观表示个体间的比较，分布形状，但其频率分布也不能估计，且原始数据同样不能显示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "418ee732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据\n",
    "data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]\n",
    "\n",
    "# 创建直方图\n",
    "plt.hist(data, bins=5)\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('数值')\n",
    "plt.ylabel('频数')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('直方图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "2abe995e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histograms \n",
    "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27]) \n",
    "plt.hist(a,bins=20) \n",
    "plt.title(\"histogram\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0127bbe9",
   "metadata": {},
   "source": [
    "## 箱线图(Box Plot)\n",
    "通过输入一维数据(如一组变量的数值),Matplotlib 可以生成箱线图，以直观地比较不同类别的数据。\n",
    "\n",
    "\n",
    "箱线图是一种反映连续型定量数据分布的中心位置和散布范围的图表，可以用来分析数据的分布情况、偏态、异常值等。它是由一组数据的最大值、最小值、中位数、上四分位数和下四分位数组成，这些数值由箱子和线条表示，其中箱子上下边界分别是第三四分位数（Q3）和第一四分位数（Q1），中间的线是中位数，箱子上下两条线分别是最大值和最小值，而异常值则以点的形式表示在箱子之外的区域。\n",
    "\n",
    "要读取箱线图的信息，可以按照以下步骤进行：\n",
    "\n",
    "观察箱体的位置和大小：箱体的上下边界分别代表第三四分位数（Q3）和第一四分位数（Q1），可以反映数据的分散程度。箱体的大小表示了数据集中各组的分布情况和集中趋势。\n",
    "\n",
    "关注中位数：箱线图中的中位数是数据集的中间值，可以帮助我们了解数据的集中趋势。\n",
    "\n",
    "寻找异常值：异常值是超出箱体范围的点，它们可能代表数据中的离群值或错误值。\n",
    "\n",
    "比较不同类别的数据：如果有多个箱线图进行比较，可以观察不同类别之间的差异，以及每个类别内部的分布情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "8f5aadfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAETCAYAAADETubIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAATzElEQVR4nO3df7DddZ3f8eeLm+CyoJiYK8qOIVJTHbcSjdc0sZQNu2FXkHURrWz91XbdCXXYnaptByxxVcr+qLVohwpratrZ0oKyrVBr18qyQzR2YODGlR9L3dqxwIqwhiYhwqACvvvH95vNncu595zEk3vDJ8/HzJl8z/m+zznvnLn3dT/38/1+7ydVhSTp2e2YxW5AkvSTM8wlqQGGuSQ1wDCXpAYY5pLUAMNcR7wk70zyqVmPXZjkHw+ofUOSV864/9YkP5dkUb7Wk7w5yXvn2X9pkpMWsie1KZ6aqCNZkhcB08BTwPf6h/858G+B3f1jxwCfAP4rcDfwrqr6ev/8TwD3ASuAfVX1L2e89teBJcCP5mnhJVX1V2Gb5Frgbwx4zgRwf1WdN6v/G/q+3gmcBnwTuLuqLur37wAuqKrvDv80pLktWewGpLkkOQX4AvBx4FG6MPwA8Hng94FPATcDU0DRBebngR8k+YWq+hPgceAHwO8Bb0myrqpu79/iSeD8qrovyeuBDwHnVD/CSbKE7gfBX6mqtx9E/yuBM4G/C1wPfKOqzuj33Qb8OvA08OOD+FikgQxzHcmeoAvyu4Hz6YJ9EtgLfAP4m8B2YAPwEPBe4BzgM8BdSV4AvB74GeBtdCP5nwZmhjlJTqQb6T8B3JHkFcDFwKfpfiM4VB8GHquqHyRZCzyV5J3At4Dv0/2gkcbCMNcRq6q+B1yT5A3AKVV1S5IzgFOBn+rLvg38DvA54MXADuAv6UbCfw/4c2BHVX24nzd/bZIXVNX/658/Afw3uqDfAewfMV/NjGNKSZ5L90PgR3S/BcxlCfCrdD90NtH9VgDwZuAOYD3ws3Qjcuc4NTaGuY5YSV4C3AC8EHhOP8d9OXAc8KK+bAXd6PmqqroyyefoRsQPAF8E3gGcn+RvASf0j28B9of508BvAo8AXwPeB7yxqn4886BpVX0/yWuAH86YhnkrcHpVva+/n/49nuh72gz86yQ/A7yHLsjfTPcbwWvG90lJns2iI1hV/UVVTQEPAvcCnwS+Sxe80/3t7v3lSS7pa39IN/r91f55TwJvAv4T8Mmq+t8z3uYlwBXAfwCuBd4KXJjkW8AvzOrnB8CyJNuTPGf/40mWJdkGTFTV96vqqar6ZlV9uS85nW7K5ga63xZuRxozw1xHtCTnA8+jO2vlH9IdUHwt3QHN3wP+aV96EvCPgLV0Z7tMVNVvVNWVdCP59/a32fPUfwG8m25K5M/ppmy+SndQdfuAlj4ITFfVD/c/UFV76EbjVw76P1TV56rqo3RBfjKwk26aaGLEj0EaymkWHbH688X/DfDPgI10B0FfAby8qv5+X/My4DNV9RDw4iTHA68G/jpwWz9Vcj/wfrqg3zXzLQCq6sEkFwNvAf4L8JWqerQ/myUz+nk98CsMniJ5P/CVJBdW1afn+C+9p++1kmwGvnNwn4g0N0fmOpL9mC4kHwaOqaqH6Q5Anp3ka0m+BnwWOCbJpiQP0o2q3wbsTfIq4E/ownwv3Zz1byX5tf71l+5/o6r6F8C5wGPAy2f0MHPA80ngvVW1f3R/7IznP0l3CuKbZl2gtCSd9wO/BPxu//h3gZfSHSid7zx3aSSOzHXEqqpvAt9M8sscCM5jgS/NGpn/R7opkdftv/gmya/QHQB9X1XdkGQV8D/opjn2j6yXAH+U5BlhmuS3ZtTs97f3T68k+TT9/PqMfu8H3jjrpZ4LnAL8MvBLM34QHAP8Md1vAbtH/EikOXkFqI54/cHG46pq70E+79iq+tHM+8DzquqR/v4LgL1V9fQh9LQceKKqnjjY50qHg2EuSQ1wzlySGmCYS1IDDHNJasCinM2yYsWKWrVq1WK8tSQ9a+3cufORqpoctG9RwnzVqlVMT08vxltL0rNWkvvn2uc0iyQ1wDCXpAYY5pLUAMNckhpgmEtSAwxzSWrAvGGeZEmSB/qVVbb3f1J0UN22JLcm2XJ42pQkzWfYyPw04Lqq2tjf7p5d0K8EM1FVG4BTk6w+HI1KkuY2LMzXA+cmub0ffQ+6yGgj3UroADfRrXf4DEk2J5lOMr1r165BJdK8li9fTpImbsuXL1/sj1ONGRbmdwCbqmod3aos5wyoOZ5uEV2A3XRrMT5DVW2tqqmqmpqcHHg1qjSvPXv2UFVN3Pbs2bPYH6caM+xy/rtmLFw7DQyaQnmMbsFcgBPwoKokLbhhwXtNkjVJJoDzgDsH1OzkwNTKGuC+sXUnSRrJsJH5ZcC1dCuUfwH4bpLLq2rmWSs3AjuSnAycTTfPLklaQPOGeVXdQ3dGy0xbZtXsS7IROAv4WFU9Os4GJUnDjeVP4FbVHg6c0SJJWmAerJSkBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQGGuSQ1wDCXpAYY5pLUAMNckhpgmEtSAwxzSWqAYS5JDTDMJakBI4V5kpOS/Okc+5YkeSDJ9v72qvG2KEkaZtTFKT7OgUWbZzsNuK6qLh5PS5KkgzV0ZJ7k54HHgYfnKFkPnJvk9iTbkoxl9SJJ0ujmDfMkxwIfAi6Zp+wOYFNVrQOWAufM8Vqbk0wnmd61a9eh9itJGmDYyPwS4Kqq2jtPzV1V9VC/PQ2sHlRUVVuraqqqpiYnJw++U0nSnIaF+SbgoiTbgVcn+cyAmmuSrEkyAZwH3DneFiVJw8w7v11VZ+zf7gP9iiSXV9WWGWWXAdcCAb5QVTcfjkYlSXMb+WBlVW3sN7fMevweujNaJEmLxIuGJKkBhrkkNcAwl6QGGOaS1ADDXJIaYJhLUgMMc0lqgGEuSQ0wzCWpAYa5JDXAMJekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQEjhXmSk5L86Tz7tyW5NcmWuWokSYfPqCPzjwPHDdqR5Hxgoqo2AKcmWT2u5iRJoxm6BmiSnwceBx6eo2QjcH2/fRNwOvCtAa+zGdgMsHLlykNoVUe7+vDz4CMnLnYbY1Efft5it6DGzBvmSY4FPgS8GbhxjrLjgQf77d3A2kFFVbUV2AowNTVVh9CrjnL56D6q2vjSSUJ9ZLG7UEuGTbNcAlxVVXvnqXmMA1MwJ4zwmpKkMRsWvJuAi5JsB16d5DMDanbSTa0ArAHuG1t3kqSRzDvNUlVn7N/uA/2KJJdX1cyzVm4EdiQ5GTgbWH8Y+pQkzWPkKZGq2lhV984KcqpqH91B0NuAM6vq0fG2KEkaZujZLKOoqj0cOKNFkrTAPFgpSQ0wzCWpAYa5JDXAMJekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQGGuSQ1wDCXpAYY5pLUAMNckhowUpgnWZ7krCQrDndDkqSDNzTMkywDvgisA25JMjmgZkmSB5Js72+vOgy9SpLmMMriFKcBH6iq2/pgXwt8eUDNdVV18bgblCQNN3RkXlVf6YP8DLrR+a0DytYD5ya5Pcm2JGNZwUiSNJpR58wDXADsAZ4cUHIHsKmq1gFLgXMGvMbmJNNJpnft2vUTtCxJmm2kMK/ORcBdwJsGlNxVVQ/129PA6gGvsbWqpqpqanLyGdPukqSfwCgHQC9O8u7+7vOBvQPKrkmyJskEcB5w57galCQNN8rIfCvwriRfBSaA7yS5fFbNZcA1wDeAW6vq5rF2KUma19ADlVW1Bzhr1sNbZtXcQ3dGiyRpEXgFqCQ1wDCXpAYY5pLUAMNckhpgmEtSAwxzSWqAYS5JDTDMJakBhrkkNcAwl6QGGOaS1ADDXJIaYJhLUgMMc0lqgGEuSQ0wzCWpAaMu6Lw8yVlJVhzuhiRJB2+UNUCXAV8E1gG3JBm4GnOSbUluTbJl0H5J0uEzysj8NOADVfXbwJeBtbMLkpwPTFTVBuDUJKvH26YkaT5Dw7yqvlJVtyU5g250fuuAso3A9f32TcDpY+tQkjTU0AWdAZIEuADYAzw5oOR44MF+ezeDR++bgc0AK1euPJReJbovxWe/ZcuWLXYLasxIB0CrcxFwF/CmASWPAcf12ycMet2q2lpVU1U1NTk5cNpdmldVLchtId5r9+7di/xpqjWjHAC9OMm7+7vPB/YOKNvJgamVNcB9Y+hNkjSiUUbmW4F3JfkqMAF8J8nls2pu7GuuAN4G/PexdilJmtfQOfOq2gOcNevhLbNq9iXZ2Nd9rKoeHVeDkqThRjoAOoo+9K8fWihJGjsv55ekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQGGuSQ1wDCXpAYY5pLUAMNckhpgmEtSAwxzSWqAYS5JDTDMJakBhrkkNWDo4hRJTgQ+S7dk3OPABVX1o1k1S4Bv9zeA36yqu8fcqyRpDqOMzN8BXFFVvwg8DLxhQM1pwHVVtbG/GeSStIBGWQP0qhl3J4HvDShbD5yb5EzgbuDCqnpqPC1KkoYZec48yQZgWVXdNmD3HcCmqloHLAXOGfD8zUmmk0zv2rXrkBuWJD3TSGGeZDlwJfBrc5TcVVUP9dvTwOrZBVW1taqmqmpqcnLykJqVJA02NMyTHAv8IfDBqrp/jrJrkqxJMgGcB9w5vhYlScMMnTMH3gOsBS5NcilwC7C0qrbMqLkMuBYI8IWqunnsnUqS5jTKAdCrgauH1NxDd0aLJGkReNGQJDXAMJekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQGGuSQ1wDCXpAYY5pLUAMNckhpgmEtSAwxzSWqAYS5JDTDMJakBhrkkNWCUNUBPTPKlJDcluaFfE3RQ3bYktybZMmi/JOnwGWVk/g7giqr6ReBh4A2zC5KcD0xU1Qbg1CSrx9umJGk+o6wBetWMu5PA9waUbQSu77dvAk4HvjWzIMlmYDPAypUrD6FVSdJcRp4zT7IBWFZVtw3YfTzwYL+9GzhpdkFVba2qqaqampycPKRmJUmDDR2ZAyRZDlwJvGWOkseA4/rtE/DAqiQtqFEOgB4L/CHwwaq6f46ynXRTKwBrgPvG0p0kaSSjjMzfA6wFLk1yKXALsLSqZp61ciOwI8nJwNnA+nE3Kkma2ygHQK8Grh5Ssy/JRuAs4GNV9ehYupMkjWSkOfNRVNUeDpzRIklaQB6olKQGGOaS1ADDXJIaYJhLUgMMc0lqgGEuSQ0wzCWpAYa5JDXAMJekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNMMwlqQEjhXmSk5LsmGf/kiQPJNne3141vhYlScMMXZwiyTLgD4Dj5yk7Dbiuqi4eV2OSpNGNMjJ/GrgA2DdPzXrg3CS3J9mWZGwrGEmShhsa5lW1b4Q1Pe8ANlXVOmApcM7sgiSbk0wnmd61a9ehdStJGmhcB0DvqqqH+u1pYPXsgqraWlVTVTU1OTk5preVJMH4wvyaJGuSTADnAXeO6XUlSSM46DBP8sokl896+DLgGuAbwK1VdfMYepMkjWjkA5VVtbH/915gy6x999Cd0SJJWgReNCRJDTDMJakBhrkkNcAwl6QGGOaS1ADDXJIaYJhLUgMMc0lqgGEuSQ0wzCWpAYa5JDXAMJekBhjmktQAw1ySGmCYS1IDDHNJaoBhLkkNGCnMk5yUZMeQmm1Jbk2yZb46SdL4DQ3zJMuAPwCOn6fmfGCiqjYApyZZPb4WJUnDjDIyfxq4ANg3T81G4Pp++ybg9J+sLUnSwRi6oHNV7QNIMl/Z8cCD/fZuYO3sgiSbgc0AK1euPNg+pUMy5Ot2bM+rqkN6H2lcxnUA9DHguH77hEGvW1Vbq2qqqqYmJyfH9LbS/KpqQW7SYhtXmO/kwNTKGuC+Mb2uJGkEQ6dZZkvySuDtVTXzrJUbgR1JTgbOBtaPpz1J0ihGHplX1cb+33tnBfn+efWNwG3AmVX16Bh7lCQNcdAj87lU1R4OnNEiSVpAXgEqSQ0wzCWpAYa5JDXAMJekBmQxLnhIsgu4f8HfWBrNCuCRxW5CGuCUqhp41eWihLl0JEsyXVVTi92HdDCcZpGkBhjmktQAw1x6pq2L3YB0sJwzl6QGODKXpAYY5tIMo6x3Kx2JDHOpN8p6t9KRyjCXDhhlvVvpiDS2P4ErPduNuN6tdERyZC5JDTDMJakBhrkkNcCLhiSpAY7MJakBhrkkNcAwl6QGGObSAEl+asb20iRLF7MfaRjDXEeVJJclOTPJbye5JMlzk3w5ycSs0huT/FySVcA/AP5dklVJ/loSL7bTEccvSh01kpxAd6n+BuCFwIuAU4DHq+rpJPsHNy8Ffgg8B/g7wOv67bfSfc98Cvj+wnYvzc+RuY4mJwIvAD4IvIJu0ebfAF6W5KvAd4Ap4HeA/wXcDJwDvKavPxe4o6oMch1xHJnraPI0sAr4V8DL6UbmpwGXAv8HuJBupL4G+L9V9eMkxwPv6p//RmDZAvcsjcSRuY4mS4CPAkuBK4BPAA8BrwVWAt8G/gx434znHAe8rL+9cAF7lQ6KI3MdTU4BLgdW042+X003P/75fv+XqureJD894zkvBn69334R8McL06p0cAxzHTWq6n8muR5YD3wJ+LOqejLJ14HzgMsGPO0R4MZ+e91C9CkdCsNcR5vfBx4HtgD3J3kp8LN0Z6+sBaaBAMf0pys+Cnytf+7zAZJMVNXTC9y3NC/DXEeNflm4q+nmxtcDrwT+PfBPgL8E/nOSt9OdhrgC+CNgF/CRGS/zOrrvm88uWOPSCPyriTqqJFlSVU/12wGO2T/KTpLyG0LPUoa5JDXAUxMlqQGGuSQ1wDCXpAYY5pLUAMNckhrw/wHtFXJN8i2C7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据\n",
    "data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]\n",
    "\n",
    "# 创建箱线图\n",
    "plt.boxplot(data)\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('数值')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('箱线图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "ad0892a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x28848d62790>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d62b20>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d7b100>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d7b490>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d85a30>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d85dc0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x28848d62eb0>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d6f280>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d7b820>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d7bbb0>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d92190>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d92520>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x28848d62400>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d6fd30>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d856a0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x28848d6f610>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d7bf40>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d928b0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x28848d6f9a0>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d85310>,\n",
       "  <matplotlib.lines.Line2D at 0x28848d92c40>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD2CAYAAAD24G0VAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAALBUlEQVR4nO3dXYjl913H8c/XPEComu6SIUVwuhQkFz7VcKgGbEhKDfGiFrVlC6EFmzIRQr3wxoYorQURSslNYEOmTUCoWMyFhYpihHa1QoNMKHhRFXvRIMKSqU2ampuKfL3YjR3WzZk5//nPnPnNvF4wcOY8zHzJJG9++T9WdweAcfzIugcAYDXCDTAY4QYYjHADDEa4AQZz81H/gjvuuKMvXLhw1L8G4FR58cUXv9PdGzd67cjDfeHChezs7Bz1rwE4VarqpTd7zaYSgMEIN8BghBtgMMINMBjhBhiMcAMMRrgBBiPcAIM58hNwAKaoqll+zmm854BwAyfSfsGtqlMZ5YNYeVNJVZ2rqr+qqp2qevoohgLgzU3Zxv3hJH/a3YskP1ZVi5lnAmCJKeH+zyQ/U1VvTfKTSf591okAWGpKuP8hyduT/E6Sf07y3evfUFVb1zal7Ozu7h5yRAD2mhLuTyb57e7+dJJ/SfJb17+hu7e7e9Hdi42NG15OFoCJpoT7XJKfraqbkvxikrO5WxdgTaaE+4+TbCf5XpLzSf5s1okAWGrl47i7+x+T/PQRzALAATjlHWAwwg0wGOEGGIxwAwxGuAEGI9wAgxFugMEIN8BghBtgMMINMBjhBhiMcAMMRrgBBiPcAIMRboDBCDfAYIQbYDCHCndVXaqq9801DAD7mxzuqnp3krd195dnnAeAfUwKd1XdkuRzSb5dVe+fdyQAlpm64v5Ikm8m+UySd1XVx/e+WFVbVbVTVTu7u7uHnRGAPaaG+xeSbHf3lSRfSHL/3he7e7u7F9292NjYOOyMAOwxNdzfSvKOa48XSV6aZxwA9nPzxM89k+TZqvpQkluSfGC+kQBYZlK4u/v7ST448ywAHIATcAAGI9wAgxFugMEIN8BghBtgMMINMBjhBhiMcAMMRrgBBiPcAIMRboDBCDfAYIQbYDDCDTAY4QYYjHADDEa4AQYj3ACDmRzuqrqzqr4x5zAA7O8wK+7PJrltrkEAOJhJ4a6q9yR5PcmVeccBYD8rh7uqbk3yB0k+seQ9W1W1U1U7u7u7h5kPgOtMWXF/Isml7n71zd7Q3dvdvejuxcbGxuThAPj/poT7vUkerarLSd5ZVZ+fdyQAlrl51Q90971vPK6qy939sXlHAmCZlcO9V3ffN9McMLuqmuXndPcsPwfmcqhww0l2kOBWlTAzHGdOAgxGuAEGI9wAgxFugMEIN8BghBtgMMINMBjhBhiMcAMMRrgBBiPcAIMRboDBCDfAYIQbYDDCDTAY4QYYzKQbKVTV7Um+mOSmJK8nudjdP5hzMABubOqK+6EkT3T3A0muJHlwvpEAWGbSiru7L+35diPJy/OMc/LMcd9Ct8YC5nSoe05W1T1JznX3C9c9v5VkK0k2NzcP8yvWbr/oumchcNwm75ysqvNJnkzy0etf6+7t7l5092JjY+Mw8wGn0Pnz51NVh/pKcuifcf78+TX/k5hm6s7JW5M8l+Sx7n5p3pGA0+6VV145Ef+nOsem0HWYuuJ+OMndSR6vqstVdXHGmQBYYurOyaeSPDXzLAAcgBNwAAYj3ACDEW6AwQg3wGCEG2Awwg0wGOEGGIxwAwxGuAEGI9wM6yRcqGjUixQxtkNd1hXW6SRcqGjUixQxtjO94j4JKzarNmBVZ3rFfRJWbIlVG7CaM73iBhiRcAMMRrgBBiPcAIMRboDBCDfAYCaHu6qeqaqvV9XvzzkQAMtNOo67qn4jyU3dfU9VPVtVP9Xd/zbzbEeuP/njyaduX/cYV+cAOKCpJ+Dcl+TPrz1+PskvJ/m/cFfVVpKtJNnc3DzEeEer/vC1E3MCTn9q3VPA8bFoOpyp4X5Lkv+49vi7Se7e+2J3byfZTpLFYrH+MgInikXT4UwN938lue3a4x+NnZyswUlYtY26YmNsU8P9Yq5uHnkhyc8n+dfZJoIDOgmrtlFXbIxtari/lORrVfUTSX41yS/NNhEAS03axNHdr+XqDsoXktzf3d+bcygA3tzky7p29yv54ZElABwTOxUBBnOmb6SQnIybGJw7d27dIwADOdPhnuOIhKpa+5ENwNliUwnAYIQbYDDCDTAY4QYYjHADDEa4AQYj3ACDOdPHcTO+dZ9A5eQp1kG4GZYTqDirbCoBGIxwAwxGuAEGI9wAg1lp52RV3Z7ki0luSvJ6kovd/YOjGAyAG1t1xf1Qkie6+4EkV5I8OP9IJ0tVLf066HsA5rLSiru7L+35diPJyzd6X1VtJdlKks3NzcnDnQQOFQNOmqXhrqqnk9y156mvdPenq+qeJOe6+4Ubfa67t5NsJ8lisVA+gBktDXd3P3L9c1V1PsmTSX7zqIYC4M2ttI27qm5N8lySx7r7paMZCYBlVt05+XCSu5M8XlWXq+riEcwEwBKr7px8KslTRzQLcIachCOuRr1ImItMAcfOBcIOx5mTAIMRboDBCDfAYIQbYDDCDTAY4QYYjHADDEa4AQYj3ACDEW6AwQg3wGCEG2Awwg0wGOEGGIxwAwxGuAEGMyncVXVnVX1j7mEA2N/UO+B8Nsltcw4CczvorbH2e99ZvcsKJ9fK4a6q9yR5PcmV+ceB+Qgup9XScFfV00nu2vPUV5Lcn+TXk3xpyee2kmwlyebm5qGHBOCHlm7j7u5Huvu+N76uPX2pu1/d53Pb3b3o7sXGxsZMowKQrL5z8r1JHq2qy0neWVWfn38kAJZZaRt3d9/7xuOqutzdH5t/JACWmXwc955NJwAcIyfgAAxGuAEGI9wAgxFugMEIN8BghBtgMMINMBjhBhiMcAMMRrgBBiPcAIMRboDBCDfAYIQbYDDCDTAY4QYYjHADDEa4AQYzKdxVdamq3jf3MADsb+VwV9W7k7ytu798BPMAsI+Vwl1VtyT5XJJvV9X7l7xvq6p2qmpnd3f3sDMCsMfNy16sqqeT3LXnqa8m+WaSzyT5eFVtdveT13+uu7eTbCfJYrHo+cYFYOmKu7sf6e773vhKspFku7uvJPlCkvuPYUYA9lh1G/e3krzj2uNFkpfmHQeA/SzdVHIDzyR5tqo+lOSWJB+YfyQAllkp3N39/SQfPKJZADgAJ+AADEa4AQYj3ACDEW6AwQg3wGCEG2Awwg0wGOEGGIxwAwxm1VPeAY5FVc3ynu7Td4FS4QZOpNMY3LnYVAIwGOEGGIxwAwxGuAEGI9wAgxFugMEIN8BghBtgMHXUB7lX1W5O993g70jynXUPwWT+fuM67X+7t3f3xo1eOPJwn3ZVtdPdi3XPwTT+fuM6y387m0oABiPcAIMR7sPbXvcAHIq/37jO7N/ONm6AwVhxAwxGuIHhVNX5qvqVqrpj3bOsg3AfQlXdWVVfW/ccrKaqbq+qv66q56vqL6rq1nXPxMFV1bkkf5nkXUm+WlU3PNb5NBPuia79y/MnSd6y7llY2UNJnujuB5JcSfLgmudhNT+X5He7+4+S/E2Su9c8z7ET7un+J8nFJK+texBW092Xuvtvr327keTldc7Darr777r7haq6N1dX3V9f90zHzT0nJ+ru15KD3ayUk6mq7klyrrtfWPcsrKau/od3MckrSf57zeMcOytuzqSqOp/kySQfXfcsrK6vejTJPyX5tXXPc9yEmzPn2s7I55I81t2n+QJop1JV/V5VfeTat29N8ur6plkP4eYsejhXd2g9XlWXq+riugdiJdtJPlxVf5/kpiTPr3meY+fMSYDBWHEDDEa4AQYj3ACDEW6AwQg3wGCEG2Aw/wvg8UAt0z03IQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box Plot using Matplotlib\n",
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "# rectangular box plot\n",
    "plt.boxplot(data,vert=True,patch_artist=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0caec104",
   "metadata": {},
   "source": [
    "## 热力图(Heat map)\n",
    "\n",
    "通过输入一维数据(如一组变量的数值),Matplotlib 可以生成热力图，显示数据之间的相关性。\n",
    "\n",
    "热力图是一种数据可视化技术，它通过对色块着色来显示数据的统计图表，可以直观地反映出数据分布和变量之间的相关性。此外，热图还可以用于分析基因、蛋白质、组织等的表达情况，以及质量控制和差异数据的展现。\n",
    "\n",
    "要读取热力图的信息，可以按照以下步骤进行：\n",
    "\n",
    "观察颜色深浅：颜色越深代表数值越大，颜色越浅代表数值越小。颜色的深浅反映了数据的大小或密度。\n",
    "\n",
    "寻找热点和冷点：热点和冷点代表了数据集中的某些特殊值或者异常值。这些点的值可能远远大于或小于其他值，对于找出数据集中的极端值非常有帮助。\n",
    "\n",
    "分析数据分布和相关性：通过观察热力图中不同区域的色块分布和连接情况，可以了解到数据的整体分布和各变量之间的相关性。\n",
    "\n",
    "对比多个热力图：如果有多个热力图进行比较，可以观察到不同数据集或者不同条件下的数据分布和相关性的变化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c64025d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成随机数据\n",
    "data = np.random.rand(10, 10)\n",
    "\n",
    "# 创建热力图\n",
    "plt.imshow(data, cmap='hot', interpolation='nearest')\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('X轴')\n",
    "plt.ylabel('Y轴')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('热力图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b835cf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d485f4a6",
   "metadata": {},
   "source": [
    "## 小提琴图(Gir figure)\n",
    "一种用于表示数据的图形，通常用于表示分类数据。\n",
    "\n",
    "小提琴图是一种数据可视化技术，它结合了核密度图和箱线图的特征，旨在展示多组数据的分布状态以及概率密度。这种图形可以清晰地描绘出数据的分布形状和分布信息，并能够帮助我们检测到数据的离群值。\n",
    "\n",
    "分析小提琴图时，可以遵循以下步骤：\n",
    "\n",
    "观察小提琴图的形状：小提琴图中间的高耸部分代表了数据的集中分布区域，而向两侧渐趋扁平的部分则显示了数据的分散程度。\n",
    "\n",
    "对比不同颜色的小提琴图：颜色深的部分代表该类别的数据分布较为密集，而颜色浅的部分则表示数据分布较为稀疏。通过对比不同颜色的小提琴图，我们可以了解到不同类别之间的数据分布差异。\n",
    "\n",
    "寻找离群值：那些远离主流颜色分布的点，就是离群值。这些点对于数据集的分析有着重要的参考价值。\n",
    "\n",
    "对特定属性的数据进行分析：例如，我们可以将地区、性别等特征作为条件，生成对应属性的小提琴图，以此来比较不同属性下数据的分布情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "04ef717a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成随机数据\n",
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "#print(data)\n",
    "# 创建小提琴图\n",
    "plt.violinplot(data)\n",
    "\n",
    "# 设置坐标轴标签\n",
    "plt.xlabel('组别')\n",
    "plt.ylabel('数值')\n",
    "\n",
    "# 设置标题\n",
    "plt.title('小提琴图示例')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a9f0fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "13f46ddc",
   "metadata": {},
   "source": [
    "## 饼图（pie chart）\n",
    "\n",
    "表示不同数据所占总体比例。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "3464c2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pie chart\n",
    "\n",
    "# Data to plot\n",
    "labels = 'Python', 'C++', 'Ruby', 'Java'\n",
    "sizes = [215, 160, 245, 210]#python会自动计算各部分比例\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']\n",
    "explode = (0.2, 0.1, 0.1, 0)  # explode 1st slice分离度\n",
    "\n",
    "# Plot绘制\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.2f%%', shadow=False)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f0855c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
